Digital Banking: AI-augmented Product Development for Future Success

August 8, 2018 by Darrel Orsmond, Financial Services Industry Head at SAP Africa

If Banks are to play meaningful roles in the daily lives of customers, they will need to expand their paradigms beyond banking to become the glue that connects their customers to the range of suppliers of goods and services they consume every day.

Customers today are snapping up the best presented, most attractive offers, delivered at exactly the time and in the manner, that best suits them. In response, retail banks are scrambling to keep up with product innovation and delivery capabilities to meet a rapidly evolving set of customer demands.

The static product design features prevalent in most banks are not easily adapted to the virtually infinite variations needed to make a payment or deposit, secure a loan or take out insurance when integrated into a customer’s daily life. And this product centricity has largely been due to the optimal design of a bank than of customer’s needs.

There is also a confluence of forces that are disrupting retail banks in dramatic ways. Business models are changing, and banks’ ability to grow and succeed in this disruptive environment is increasingly reliant on their ability to integrate their offerings into customers’ lives. Customers’ demands for real-time delivery and conversion of financial transactions are placing pressure on providers, and it’s clear that customers care less about who fulfils their requirements as long as it is done with immediacy. In this environment of infinite and individualised customer needs, standard products, prices and offers are unlikely to suffice.

Responding to changing banking paradigms

Even in a modern digital banking setting, the core construct of an account changes little; term, interest rate, fee basis and balance. Supporting processes, such as a reliable system of record, business rules, terms and conditions, and access rules are similarly fundamental, but change little regardless of the innovation and marketing applied.

Traditional customer segmentation rules which historically defined broad customer offerings, which were then matched to a product design generally don’t work today because they are simply not dynamic enough. Today’s customer needs and measures to entice them defy the broad grouping approach because customer demands change so quickly and drastically that it becomes untenable to rely on this approach.

Data algorithms and AI which reflect the latest available customer activity have the power to constantly modify or design new offers. They can circumvent cumbersome product management processes by researching, proposing, developing and implementing customer offers – in real time. AI insights can further test individual customer responses and learn, adjust and build new offers, something that no human-led process can achieve accurately and at speed.

For example, as customers transact for travel, healthcare, insurance, consumer durables or capital goods, these transactions often require the creation of a loan or deposit as support to the offer. The AI algorithm can offer a preapproved loan for the purchase of a computer, scored as the location services identify where the customer is and what they are probably doing – for example as a customer is walking down the aisle of a retailer where computers are displayed – and apply unique risk and fee pricing that is offered on mobile and enables instant conversion. On success or failure, the AI learning can internalise the meaning of the response and adjust future offers to the individual and groups.

Toward a digital banking architecture

Modern banking products require infinite variations on offers that attract customers but still takes into account well- defined operating parameters such as threshold risk, yield, interest rate and fee bases. The prevailing industrial age models many banks still rely on are simply insufficient to achieve this.

However, four key components to digital banking may illuminate a way forward, namely:

Systems of record which create and deliver the core processes of a product offering such as the accounting, the posting, the status, the business rules and customer details and preferences

Deep analytical and AI capability to assess all the operational performance data of the business, to manage the opportunity delivery process, (leads generated through AI and the consequences), and customer facing insights (behaviour spend, forecasting, predictive analytics).

Customer omni-channel delivery systems which synchronise the customer’s interaction with the organisation and provide delivery and conversion of offers from transactions through to loans and deposits.

A sophisticated integration capability which combines with any other architecture to expose core services such as payments, identification etc.

What this means for product development is that teams of product specialists will determine the boundaries of product economics incorporating threshold interest rates and risk tolerance, revenue and cost margins for customers, and anchor rates for risk pricing and code these into the Big Data platform. AI processes will then use these, as well as all available other data, including external and social media, to craft specific offers to customers based on a range of self-learning triggers, e.g. customer activity, external activity (social media or other), or other external events which are triggered by the AI algorithms – (a ship loaded with rare-earth metals sinking in the Pacific), which affects the customer’s investment portfolio.

This sophisticated process results in customer-specific offers which are unique and are calibrated to the customer’s behavioural preferences – for example, risk averse customers who like to pay in advance and not carry any downside risk.

In this world, product owners become portfolio managers of a range of effects of the AI processes which are constantly managed by data-skilled managers, generating offers that are delivered instantaneously and overseen by much smaller teams that are highly qualified in data sciences.

It’s a fundamental rethink of how banking products are developed while also respecting the fundamental elements of what makes a bank. Are African banks ready for this paradigm shift?